one noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 33"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8068  -1.0493   0.5770   0.9415   2.5190  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.18447    0.05074   3.635 0.000277 ***
## n1           2.20269    0.13545  16.262  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2256.7  on 1998  degrees of freedom
## AIC: 2260.7
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"

## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"

## [1] "effects model, sigma= 33"
## [1] "one noise variable, logistic regression effects model, sigma= 33 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.253  -1.196   1.102   1.148   1.427  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.04177    0.04621   0.904 0.365995    
## n1           0.20597    0.05882   3.502 0.000463 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2760.1  on 1998  degrees of freedom
## AIC: 2764.1
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one noise variable, logistic regression Noised 33 train mean deviance 1.99098170430528"

## [1] "one noise variable, logistic regression Noised 33 test mean deviance 2.00767495136368"

## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3619  -1.1570   0.9662   1.1980   1.2169  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.04838    0.04731  -1.023  0.30650   
## n1          -0.06366    0.01954  -3.258  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2761.8  on 1998  degrees of freedom
## AIC: 2765.8
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"

## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"

## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.000   2.001   2.001   2.001   2.006 
## [1] 0.0009731397
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## [1] "one noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.001   2.002   2.003   2.005   2.022 
## [1] 0.003580925
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## [1] "********"
## [1] "one noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.424   3.855   3.956   3.980   4.094   4.529 
## [1] 0.2145439
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.002   2.003   2.005   2.008   2.023 
## [1] 0.004262642
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## [1] "one noise variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.000   2.000   2.000   2.001   2.006 
## [1] 0.0008414625
## [1] "********"
## [1] "*************************************************************"

one variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 5"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1243  -1.1809   0.4704   1.1554   1.5778  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4731     0.0542    8.73   <2e-16 ***
## x1            3.1777     0.2114   15.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2434.7  on 1998  degrees of freedom
## AIC: 2438.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"

## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"

## [1] "effects model, sigma= 5"
## [1] "one variable, logistic regression effects model, sigma= 5 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0770  -1.1737   0.4958   1.1624   1.6188  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4388     0.0527   8.326   <2e-16 ***
## x1            3.1337     0.2079  15.073   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2444.1  on 1998  degrees of freedom
## AIC: 2448.1
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression Noised 5 train mean deviance 1.76306565820564"

## [1] "one variable, logistic regression Noised 5 test mean deviance 1.75523069171642"

## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0811  -1.1892   0.4966   1.1600   1.5642  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.45308    0.05326   8.508   <2e-16 ***
## x1           2.99703    0.20478  14.636   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2460.2  on 1998  degrees of freedom
## AIC: 2464.2
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"

## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"

## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.736   1.759   1.771   1.770   1.780   1.801 
## [1] 0.01361791
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## [1] "********"
## [1] "one variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.738   1.760   1.770   1.771   1.781   1.803 
## [1] 0.01355715
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## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.737   1.761   1.772   1.771   1.783   1.805 
## [1] 0.01425687
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## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.739   1.760   1.772   1.774   1.785   1.824 
## [1] 0.01809216
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.981   1.984   1.986   1.986   1.988   1.991 
## [1] 0.002469481
## [1] "********"
## [1] "*************************************************************"

one variable plus noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 9"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5658  -0.9120   0.3055   0.8035   2.7112  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.68760    0.06161   11.16   <2e-16 ***
## x1           3.18452    0.23641   13.47   <2e-16 ***
## n1           2.45247    0.15572   15.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 1990.5  on 1997  degrees of freedom
## AIC: 1996.5
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"

## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"

## [1] "effects model, sigma= 9"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 9 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2017  -1.1260   0.4599   1.0784   1.9202  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.53470    0.05559   9.619  < 2e-16 ***
## x1           3.31977    0.21977  15.105  < 2e-16 ***
## n1           0.48966    0.08909   5.497 3.87e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2410.7  on 1997  degrees of freedom
## AIC: 2416.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable plus noise variable, logistic regression Noised 9 train mean deviance 1.73892471181772"

## [1] "one variable plus noise variable, logistic regression Noised 9 test mean deviance 1.79890921580638"

## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2012  -1.1757   0.5026   1.1657   1.5936  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.42346    0.05493   7.710 1.26e-14 ***
## x1           3.00699    0.20534  14.644  < 2e-16 ***
## n1          -0.05278    0.02435  -2.167   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2455.4  on 1997  degrees of freedom
## AIC: 2461.4
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"

## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"

## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.747   1.768   1.775   1.775   1.781   1.815 
## [1] 0.01165322
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.750   1.763   1.773   1.773   1.780   1.797 
## [1] 0.01142931
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.074   3.464   3.597   3.603   3.717   4.144 
## [1] 0.2241516
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.761   1.788   1.800   1.804   1.816   1.871 
## [1] 0.02436318
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.979   1.984   1.986   1.986   1.988   1.994 
## [1] 0.003256879
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## [1] "*************************************************************"